U.S. patent number 8,379,773 [Application Number 12/489,924] was granted by the patent office on 2013-02-19 for method and apparatus for enhanced channel estimation in wireless communication systems.
This patent grant is currently assigned to Telefonaktiebolaget L M Ericsson (Publ). The grantee listed for this patent is Jiann-Ching Guey, Leonid Krasny. Invention is credited to Jiann-Ching Guey, Leonid Krasny.
United States Patent |
8,379,773 |
Krasny , et al. |
February 19, 2013 |
**Please see images for:
( Certificate of Correction ) ** |
Method and apparatus for enhanced channel estimation in wireless
communication systems
Abstract
A method and apparatus for channel estimation based on
extracting channel information, including noise spectral density,
from a received signal, and advantageously exploiting that
information for improved channel estimation accuracy. One
embodiment is directed to a method of generating channel estimates
in a wireless communication receiver, for processing a received
communication signal. The method includes generating first channel
estimates from a set of pilot observations obtained from the
received communication signal, using a first channel estimation
process that is not dependent on knowledge of channel statistics.
The method further includes estimating channel statistics and a
noise variance from the first channel estimates, and generating
second channel estimates from the set of pilot observations, the
estimated channel statistics, and the estimated noise variance,
using a second channel estimation process that is dependent on
knowledge of the channel statistics.
Inventors: |
Krasny; Leonid (Cary, NC),
Guey; Jiann-Ching (Cary, NC) |
Applicant: |
Name |
City |
State |
Country |
Type |
Krasny; Leonid
Guey; Jiann-Ching |
Cary
Cary |
NC
NC |
US
US |
|
|
Assignee: |
Telefonaktiebolaget L M Ericsson
(Publ) (Stockholm, SE)
|
Family
ID: |
42808975 |
Appl.
No.: |
12/489,924 |
Filed: |
June 23, 2009 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20100322357 A1 |
Dec 23, 2010 |
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Current U.S.
Class: |
375/341; 375/265;
375/262; 375/346; 375/260; 375/349 |
Current CPC
Class: |
H04L
25/022 (20130101); H04L 25/0218 (20130101); H04L
5/0007 (20130101); H04L 25/024 (20130101); H04L
5/0048 (20130101); H04L 25/0232 (20130101) |
Current International
Class: |
H04L
27/06 (20060101) |
Field of
Search: |
;375/349,341,262,346,260,265 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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WO 2008/039026 |
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Apr 2008 |
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WO |
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Other References
Guey et al. "low complexity channel estimation for minimizing edge
effect in OFDM systems";2007;pp. 1440-1444. cited by
examiner.
|
Primary Examiner: Washburn; Daniel
Assistant Examiner: Guarino; Rahel
Claims
What is claimed is:
1. A method of generating channel estimates in a wireless
communication receiver, for processing a received Orthogonal
Frequency-Division Multiplexing (OFDM) communication signal, the
method comprising: generating first channel estimates jointly for a
group of OFDM subcarriers in the received OFDM communication signal
from a set of pilot observations obtained from the received OFDM
communication signal, using a first channel estimation process not
dependent on knowledge of channel statistics; estimating channel
statistics and a noise variance from the first channel estimates;
and generating second channel estimates jointly for the group of
OFDM subcarriers from the set of pilot observations, the estimated
channel statistics, and the estimated noise variance, using a
second channel estimation process dependent on knowledge of the
channel statistics.
2. The method of claim 1, further comprising generating revised
estimates of the channel statistics and noise variance from the
second channel estimates, and generating revised second channel
estimates from the set of pilot observations, the revised estimated
channel statistics, and the revised estimated noise variance.
3. The method of claim 1, wherein the received communication signal
comprises the OFDM signal including a number of pilot symbols at
given sub-carrier frequencies within an OFDM frequency band, and
wherein the pilot observations correspond to the pilot symbols.
4. The method of claim 3, wherein generating the first channel
estimates includes determining channel taps at which to generate
respective ones of the first channel estimates by transforming
received pilot symbols into the time domain to obtain a set of
channel taps, and selecting channel taps in the set that are above
a defined strength threshold.
5. The method of claim 3, wherein generating the first channel
estimates comprises translating the pilot observations into the
time domain and generating time-domain channel estimates therefrom,
translating the time-domain channel estimates back into the
frequency domain, to obtain the first channel estimates, and
estimating the channel statistics in the frequency domain, based on
the first channel estimates.
6. The method of claim 5, wherein generating the second channel
estimates comprises computing a linear interpolation filter in the
frequency domain, and generating the second channel estimates for
one or more data sub-carrier frequencies within the OFDM frequency
band, which are different than the given sub-carrier frequencies of
the pilot symbols.
7. The method of claim 6, wherein generating the second channel
estimates comprises generating the second channel estimates in a
Bayesian estimation process.
8. The method of claim 1, wherein generating the first channel
estimates comprises generating the first channel estimates in a
Maximum Likelihood (ML) estimation process, or in a Least Squares
Estimation (LSE) process, based on the set of pilot observations
and corresponding known nominal pilot symbol values.
9. The method of claim 1, wherein generating the second channel
estimates comprises generating the second channel estimates in a
Maximum a Posteriori (MAP) estimation process, based on the
estimated channel statistics, the estimated noise variance, and the
set of pilot observations.
10. The method of claim 1, wherein estimating the channel
statistics comprises estimating a frequency-domain channel
correlation matrix.
11. A receiver circuit for generating channel estimates for
processing a received Orthogonal Frequency-Division Multiplexing
(OFDM) communication signal in a wireless communication receiver,
said receiver circuit comprising: a first channel estimator
configured to generate first channel estimates jointly for a group
of OFDM subcarriers in the received OFDM communication signal from
a set of pilot observations obtained from the received OFDM
communication signal, using a first channel estimation process not
dependent on knowledge of channel statistics; a statistical
estimator configured to estimate channel statistics and a noise
variance from the first channel estimates; and a second channel
estimator configured to generate second channel estimates jointly
for the group of OFDM subcarriers from the set of pilot
observations, the estimated channel statistics, and the estimated
noise variance, using a second channel estimation process dependent
on knowledge of the channel statistics.
12. The receiver circuit of claim 11, wherein the statistical
estimator is further configured to generate revised estimates of
the channel statistics and the noise variance from the second
channel estimates, and wherein the second channel estimator is
further configured to generate revised second channel estimates
from the set of pilot observations, the revised estimated channel
statistics, and the revised estimated noise variance.
13. The receiver circuit of claim 11, wherein the wireless
communication receiver comprises the OFDM receiver, and wherein the
received communication signal comprises an OFDM signal including a
number of pilot symbols at given sub-carrier frequencies within an
OFDM frequency band, and wherein the pilot observations correspond
to the pilot symbols.
14. The receiver circuit of claim 13, wherein the first channel
estimator is configured to generate the first channel estimates by
determining channel taps at which to generate respective ones of
the first channel estimates, based on transforming received pilot
symbols into the time domain to obtain a set of channel taps, and
selecting channel taps in the set that are above a defined strength
threshold.
15. The receiver circuit of claim 13, wherein the first channel
estimator is configured to generate the first channel estimates by
translating the pilot observations into the time domain and
generating time-domain channel estimates therefrom, translating the
time-domain channel estimates back into the frequency domain, to
obtain the first channel estimates, and wherein the statistical
estimator is configured to estimate the channel statistics in the
frequency domain, based on the first channel estimates.
16. The receiver circuit of claim 15, wherein the second channel
estimator is configured to generate the second channel estimates by
computing a linear interpolation filter in the frequency domain,
and generating the second channel estimates for one or more data
sub-carrier frequencies within the OFDM frequency band, which are
different than the given sub-carrier frequencies of the pilot
symbols.
17. The receiver circuit of claim 16, wherein the second channel
estimator is configured to generate the second channel estimates in
a Bayesian estimation process.
18. The receiver circuit of claim 11, wherein the first channel
estimator is configured to generate the first channel estimates in
a Maximum Likelihood (ML) estimation process, or in a Least Squares
Estimation (LSE) process, based on the set of pilot observations
and corresponding known nominal pilot symbol values.
19. The receiver circuit of claim 11, wherein the second channel
estimator is configured to generate the second channel estimates in
a Maximum a Posteriori (MAP) estimation process, based on the
estimated channel statistics, the estimated noise variance, and the
set of pilot observations.
20. The receiver circuit of claim 11, wherein the statistical
estimator is configured to estimate a frequency-domain channel
correlation matrix, as said channel statistics.
Description
FIELD OF THE INVENTION
The present invention generally relates to wireless communications,
and particularly relates to generating channel estimates in a
wireless communication receiver.
BACKGROUND
Wireless communication receivers estimate propagation channel
characteristics and use the estimates to compensate received
signals for channel-induced distortion. More advanced receiver
types base interference suppression processing on accurate channel
estimation. However, generating accurate channel estimates is
challenging, particularly with the growing complexity of
communication signal structures.
Multiple-Input-Multiple-Output (MIMO) systems, for example, pose
particular challenges, where channel estimation generally must
account for the interplay between N.sub.tx transmit antennas and
N.sub.rx receive antennas. With pilot-assisted channel estimation,
the transmitter transmits a number of known (or pre-determined)
symbols from each transmit antenna, thereby allowing estimation of
the MIMO channel by the receiver. The LTE standards, as developed
by the Third Generation Partnership Project (3GPP), use
pilot-assisted channel estimation.
LTE uses an Orthogonal Frequency Division Multiplex (OFDM) carrier
signal comprising a plurality of narrowband sub-carriers spanning
an overall OFDM bandwidth. Resource allocations assign particular
frequencies (sub-carriers) at particular times. In this respect, an
OFDM signal "chunk" may be defined as a block of N.sub.t
consecutive OFDM symbols (along the time axis) and N.sub.f
consecutive sub-carriers (along the frequency axis).
A simplifying assumption is that the channel does not change in
time over one chunk and therefore all the pilot symbols are placed
in the first OFDM symbols of the chunk. Let
{P.sub.j(f.sub.m)}.sub.m=1.sup.M denote the subset of elements of a
chunk transmitted from transmit antenna j that are devoted to
pilots. That is, M pilot symbols will be transmitted during each
chunk from transmit antenna j. The subset of indexes
{f.sub.m}.sub.m=1.sup.M for each transmit antenna is determined by
the chosen pilot pattern in the frequency-time domain. Similarly,
let {Y.sub.i,j(f.sub.m)}.sub.m=1.sup.M denote the received signal
at the i-th receive antenna corresponding to the pilots
{P.sub.j(f.sub.m)}.sub.m=1.sup.M.
Assuming that the pilot symbols transmitted by different antennas
are orthogonal, i.e. if P.sub.j(f.sub.m) is a pilot symbol on the
j-th antenna, then P.sub.j1(f.sub.m)=0 for all j.sub.1.noteq.j.
This implies that the relationship between Y.sub.i,j(f.sub.m) and
{P.sub.j(f.sub.m)}.sub.m=1.sup.M can be described by the following
expression:
Y.sub.i,j(f.sub.m)=H.sub.i,j(f.sub.m).times.P.sub.j(f.sub.m)+V.sub.i(f.su-
b.m),1.ltoreq.m.ltoreq.M, (Eq. 1) where H.sub.i,j(f) is the
frequency response of the channel between the j-th transmit antenna
and the i-th receive antenna corresponding to the f-th sub-carrier,
and V.sub.i(f) is a spatially uncorrelated white noise at the i-th
receive antenna (antenna thermal noise+other-cell interference)
with spectral density g.sub.i.
The goal of the channel estimation is to find the estimate of the
MIMO channels H.sub.i,j(f) based on observations of
{Y.sub.i,j(f.sub.m)}.sub.m=1.sup.M and a priori knowledge of the
transmitted pilot symbols {P.sub.j(f.sub.m)}.sub.m=1.sup.M=1. One
approach is to use Maximum A Posteriori (MAP) channel estimation.
Assuming that MIMO channels have Gaussian distribution, it has been
shown that MAP channel estimation algorithm can be expressed as
.function..times..function..times..function..times. ##EQU00001##
where .sub.i,j(f) denotes the estimate of the channel
H.sub.i,j(f).
In Eq. (2), the coefficients W.sub.j(f,f.sub.m) are computed as
follows:
.function..times..function..times..function..times..function..times.
##EQU00002## where A.sub.j.sup.-1(f.sub.p,f.sub.m) are the elements
of the matrix A.sub.j.sup.-1 which is inverse to the matrix A.sub.j
with elements
A.sub.j(f.sub.p,f.sub.m)=g.sub.i.delta.(f.sub.p-f.sub.m)+P.sub.j-
*(f.sub.p)K.sub.H(f.sub.p,f.sub.m)P.sub.j(f.sub.m), (Eq. 4) and
K.sub.H(f.sub.p,f.sub.m)=E{H.sub.i,j(f.sub.p)H.sub.i,j*(f.sub.m)}
(Eq. 5) is the correlation matrix of the channel H.sub.i,j(f) in
frequency domain. From these expressions, one sees that the
MAP-based approach relies on knowledge of second-order channel
statistics, including the channel correlation matrix
K.sub.H(f.sub.p,f.sub.m), and the noise spectral density
g.sub.i.
Another well known approach to channel estimation relies on the
Maximum Likelihood (ML) algorithm. Denoting the impulse response of
the channel between the j-th transmit antenna and the i-th receive
antenna by
.function..times..function..times..times..pi..times..ltoreq..ltoreq..time-
s. ##EQU00003## the ML channel estimation algorithm can be
expressed as
.function..times..function..times..times..function..times..function..time-
s. ##EQU00004## where L is the number of channel taps, N is the
number of received samples in time domain, y.sub.i,j(n) and
P.sub.j(n) are respectively Fourier transforms of the received
signal Y.sub.i,j(f) and pilots P.sub.j(f) at time n*.DELTA.t
(.DELTA.t is a sampling interval), and F.sub.j.sup.-1(l,s) are the
elements of the matrix F.sub.j.sup.-1 which is inverse to the
matrix F.sub.j with elements
.function..times..function..times..function..times.
##EQU00005##
While the ML estimator is simpler to implement in some respects
than MAP-based estimators--e.g., ML estimation does not require a
priori knowledge of channel statistics, as does MAP estimation--ML
estimation can yield poor results in some circumstances. For
example, ML estimation does not perform particularly well for MIMO
systems with spatially distributed antennas.
SUMMARY
This document discloses a method and apparatus for channel
estimation based on extracting channel information, including noise
spectral density, from a received signal, and advantageously
exploiting that information for improved channel estimation
accuracy. One embodiment is directed to a method of generating
channel estimates in a wireless communication receiver, for
processing a received communication signal. The disclosed method
includes generating first channel estimates from a set of pilot
observations obtained from the received communication signal, using
a first channel estimation process not dependent on knowledge of
channel statistics. The method further includes estimating channel
statistics and a noise variance from the first channel estimates,
and generating second channel estimates from the set of pilot
observations, the estimated channel statistics, and the estimated
noise variance, using a second channel estimation process dependent
on knowledge of the channel statistics.
As a further advantage, one or more embodiments of the above
described method include generating revised estimates of the
channel statistics and noise variance from the second channel
estimates, and generating revised second channel estimates from the
set of pilot observations, the revised estimated channel
statistics, and the revised estimated noise variance. Iterations
beyond this second round of refinements also may be used, where the
improved statistical estimations from a preceding iteration are
used to improve channel estimation in a succeeding iteration.
Another embodiment provides a receiver circuit for generating
channel estimates, for processing a received communication signal
in a wireless communication receiver. The receiver circuit includes
first and second channel estimators, and a statistical estimator.
The first channel estimator is configured to generate first channel
estimates from a set of pilot observations obtained from a received
communication signal, using a first channel estimation process not
dependent on knowledge of channel statistics, and the statistical
estimator is configured to estimate channel statistics and a noise
variance from the first channel estimates. Further, the second
channel estimator is configured to generate second channel
estimates from the set of pilot observations, the estimated channel
statistics, and the estimated noise variance. Here, the second
channel estimator uses a second channel estimation process that is
dependent on knowledge of the channel statistics.
This example receiver embodiment, and the earlier method example,
thus may be understood as running two channel estimation processes,
where the first process does not require knowledge of the channel
statistics, and the second one does. More particularly, in at least
one embodiment, the second channel estimation process is known or
expected to provide superior channel estimation accuracy as
compared to the first channel estimation process, under at least
some conditions. The first channel estimation process, however, is
sufficiently good to bootstrap or otherwise seed the second process
with requisite statistical information.
Of course, the present invention is not limited to the above
features and advantages. Indeed, those skilled in the art will
recognize additional features and advantages upon reading the
following detailed description, and upon viewing the accompanying
drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a block diagram of one embodiment of a wireless
communication network and an associated mobile terminal that
includes a receiver circuit for channel estimation as proposed
herein.
FIG. 2 is a logic flow diagram for one embodiment of a method of
channel estimation.
FIG. 3 is a block diagram of one embodiment of a receiver circuit
configured for channel estimation.
FIG. 4 is a flow diagram illustrating one embodiment of time domain
and frequency domain processing operations, for channel estimation
as proposed herein for an OFDM received signal.
FIG. 5 is a performance diagram plotting codeword error rates for
channel estimation based on perfect channel knowledge (idealized),
based on ML estimation, and based on one embodiment of the
estimation proposed herein.
FIG. 6 is a table illustrating models and parameters associated
with the plot of FIG. 5.
DETAILED DESCRIPTION
FIG. 1 is a simplified illustration of one embodiment of a wireless
communication network 10, which includes a Radio Access Network
(RAN) 12 and a Core Network (CN) 14, which may be coupled to one or
more external networks 16. For example, the CN 14 may couple
directly or indirectly to the Internet and/or to other data
networks.
The RAN 12 includes a number of base stations 20--one is shown for
simplicity--each having one or more transmit antennas 22, for
transmitting radiofrequency signals to and receiving radiofrequency
signals from mobile terminals 30--one is shown for simplicity. The
signals are propagated over the air, and thus pass through one or
more propagation channels. The propagation channel(s) typically are
multipath, and, for MIMO implementations involving multi-antenna
transmission and reception, there may be a number of propagation
channels involved, corresponding to the different transmit/receive
antenna pairings.
Accordingly, the illustrated embodiment of the mobile terminal 30
includes one or more transmit/receive antennas 32, which are
coupled through antenna interface circuitry 34 to a transmit
circuit 36, and a receiver front-end circuit 38. Baseband
processing circuits 40 provide signal processing and control
functions for the transmitter and receiver front-end circuits 36
and 38, and may be implemented, for example, using one or more
microprocessors, digital signal processors, Application Specific
Integrated Circuits (ASICs), Field Programmable Gate Arrays, or
other digital processing circuitry.
Of particular interest, the processing circuits 40 include a
receiver circuit 42 for generating channel estimates, for
processing a received communication signal in a wireless
communication receiver, e.g., the mobile terminal 30. Such
circuitry may be pre-programmed or may operate according to stored
program instructions, which are maintained in a computer-readable
medium within the mobile terminal 30--e.g., non-volatile FLASH
memory or EEPROM.
In one or more embodiments, the receiver circuit 42 is configured
to implement a method of generating channel estimates, for
processing a received communication signal. The method includes
generating first channel estimates from a set of pilot observations
obtained from the received communication signal, using a first
channel estimation process not dependent on knowledge of channel
statistics, and estimating channel statistics and a noise variance
from the first channel estimates. The method further includes
generating second channel estimates from the set of pilot
observations, the estimated channel statistics, and the estimated
noise variance, using a second channel estimation process dependent
on knowledge of the channel statistics.
The receiver circuit 42 comprises, for example, signal processing
circuitry that is configured to carry out channel estimation
processing as proposed herein, within the mobile terminal 30.
However, it should be understood that the base station 20 also
includes (RF) transceiver circuits and associated signal processing
and control circuits. As such, those skilled in the art should
appreciate that a version of the receiver circuit 42, as adapted
for base station use, is contemplated herein.
As for the illustration, FIG. 3 depicts an embodiment of the
receiver circuit 42 in more detail. The illustrated circuitry
comprises a first channel estimator 50, a statistics estimator 52,
and a second channel estimator 54. The first channel estimator 50
is configured to generate first channel estimates from a set of
pilot observations obtained from a received communication signal,
using a first channel estimation process not dependent on knowledge
of channel statistics. The statistical estimator 52 is configured
to estimate channel statistics and a noise variance--which may be
expressed as a noise spectral density--from the first channel
estimates. Correspondingly, the second channel estimator 54 is
configured to generate second channel estimates from the set of
pilot observations, the estimated channel statistics, and the
estimated noise variance, using a second channel estimation process
dependent on knowledge of the channel statistics.
For example, in one or more embodiments, the first channel
estimator 50 is configured to generate the first channel estimates
in a Maximum Likelihood (ML) estimation process, or in a Least
Squares Estimation (LSE) process, based on the set of pilot
observations and corresponding known nominal pilot symbol values.
The ML and LSE estimation processes do not require a priori
knowledge of the channel statistics and noise variance, and thus
provide an advantageous basis for initially processing the pilot
observations to obtain initial (first) channel estimates.
Unlike the first channel estimation process, the second channel
estimation process is a Bayesian estimation process that is
dependent on a priori statistical knowledge of the channel. For
example, the second channel estimator 54 is configured to generate
the second channel estimates in a Maximum a Posteriori (MAP)
estimation process, based on the estimated channel statistics and
the estimated noise variance obtained from the first channel
estimation process, and the set of pilot observations. Thus, the
same set of pilot observations for the same received signal--e.g.,
one or more OFDM chunks--is used for two channel estimation
processes. In particular, the first channel estimation process uses
the pilot observations to estimate channel statistics and noise
variance, where, for example, the statistical estimator 52 is
configured to estimate a frequency-domain channel correlation
matrix as the channel statistics.
The estimation of channel statistics as a frequency-domain channel
correlation matrix is particularly useful in the OFDM context. For
example, the mobile terminal 30 in one or more embodiments
comprises an OFDM receiver (transceiver), and the received
communication signal comprises an OFDM signal including a number of
pilot symbols at given sub-carrier frequencies within an OFDM
frequency band. The pilot observations taken by the terminal 30
correspond to the pilot symbols.
In this context, the first channel estimator 50 is configured to
generate the first channel estimates by determining channel
taps--processing delays--at which to generate respective ones of
the first channel estimates. The estimator's determination is based
on transforming received pilot symbols into the time domain to
obtain a set of channel taps, and selecting channel taps in the set
that are above a defined strength threshold. That is, a measure of
received signal strength, or another indication of signal power at
the individual channel taps can be used to select a subset of the
channel taps for processing use.
In any case, the first channel estimator 50 in such embodiments is
configured to generate the first channel estimates by translating
the pilot observations into the time domain and generating
time-domain channel estimates therefrom, and then translating the
time-domain channel estimates back into the frequency domain, to
obtain the first channel estimates. Correspondingly, the
statistical estimator 52 is configured to estimate the channel
statistics in the frequency domain, based on the (frequency domain)
first channel estimates.
Continuing from these first channel estimates, the second channel
estimator 54 is configured to generate the second channel estimates
by computing a linear interpolation filter in the frequency domain.
The linear interpolation filter is used to generate the second
channel estimates for one or more data sub-carrier frequencies
within the OFDM frequency band, which are different than the given
sub-carrier frequencies of the pilot symbols. That is, by using the
linear interpolation filter, the second channel estimator 54 can
generate channel estimates at essentially any arbitrary frequency
within a given OFDM frequency band, meaning that it can estimate
channel response at sub-carrier frequencies, although those
frequencies are different from the pilot sub-carrier
frequencies.
FIG. 4 illustrates one embodiment of such processing, where the
dashed vertical line indicates the division between the frequency
domain and the time domain, in terms of the receiver circuit's
processing. One sees that for the OFDM example, received pilot
values--i.e., pilot symbols at given sub-carrier positions within
an OFDM chunk--are converted into the time domain via, e.g., an
Inverse Fast Fourier Transform (IFFT). The receiver circuit 42
selects channel taps within the time domain, and generates
time-domain channel estimates for the selected channel taps. These
time-domain channel estimates are then transformed back into the
frequency domain, e.g., via an FFT.
Once converted back into the frequency domain, these channel
estimates are considered the "first channel estimates," and they
are used to estimate the second-order channel statistics and noise
variance in the frequency domain. Frequency-domain processing
further includes computation of the linear interpolation filter,
and use of that filter to generate the second channel estimates
(for the OFDM data sub-carriers).
Referring back to FIG. 3 for the moment, one also sees the
indication of an optional recursive loop through the statistics
estimator 52 and the second channel estimator 54. In such
embodiments, the statistical estimator 52 is configured to generate
revised estimates of the channel statistics and the noise variance
from the second channel estimates. That is, the second channel
estimates as output from the second channel estimator 54 are used
to refine the receiver circuit's estimation of the channel
statistics and noise variance.
In turn, the second channel estimator 54 is configured to generate
revised second channel estimates from the set of pilot
observations, the revised estimated channel statistics, and the
revised estimated noise variance. Thus, the second channel
estimates are used to revise the channel statistics and noise
variance estimates, and then those revised estimates are used to
generate a revised set of second channel estimates.
Various embodiments of the receiver circuit 42 may be configured to
perform additional iterations, where the revised second channel
estimates from a prior iteration are used to generate improved
revised estimates of the channel statistics and noise variance in a
next iteration, which in turn are used to generate a revised set of
second channel estimates. Such iteration is fixed to a defined
number of runs in one embodiment, while another embodiment controls
the number of iterations based on one or more criterion, such as
the change in revised estimates between iterations.
Of course, not all embodiments iterate, and the basic improvement
in channel estimation accuracy comes from the use of the first
estimation process to gain information about (second order) channel
statistics. With this approach, an estimate of the channel between
the j-th transmit antenna and the i-th receive antenna,
H.sub.i,j(f), can be calculated quickly, in a few strategic steps.
As noted, the first step is performing channel estimation using an
estimation process that does not depend on knowledge of the channel
statistics. The ML and LSE algorithms are two such examples. Taking
ML for example, the channel estimate h.sub.i,j(l) is transformed to
the frequency domain, creating the ML estimate of the channel
frequency response:
.cndot..function..times..function..times..times..times..times..times..pi.-
.times..times. ##EQU00006##
At the next step, the estimate from (Eq. 9) is used to estimate the
channel correlation matrix as
.cndot..function..times..times..cndot..function..cndot..function..times.
##EQU00007## and noise spectral density
.cndot..times..times..times..function..function..times..cndot..function..-
times. ##EQU00008##
Substituting (Eq. 10) and (Eq. 11) into (Eq. 2) gives a new MAP
estimate as:
.cndot..function..times..cndot..function..times..function..times.
##EQU00009## where the coefficients .sub.j(f,f.sub.m) are computed
as follows:
.cndot..function..times..cndot..function..times..function..times..cndot..-
times. ##EQU00010## where A.sub.j.sup.-1(f.sub.p,f.sub.m) are the
elements of the matrix A.sub.j.sup.-1 which is inverse to the
matrix A.sub.j with
.sub.j(f.sub.p,f.sub.m)=.sub.i.delta.(f.sub.p-f.sub.m)+P.sub.p*(f.sub.p).-
sub.H(f.sub.p,f.sub.m)P.sub.j(f.sub.m). (Eq. 14)
In assessing expected performance for the first/second channel
estimation processing proposed herein, the inventors based
simulations on more than 100 different channel realizations, using
average codeword error rate as the performance measure. FIG. 5
illustrates the simulated performance, as compared to performance
based on perfect channel knowledge, and performance based on ML
channel estimation only.
In more detail, the evaluation considered a MIMO system with two
mobile terminals 30 transmitting to two base stations 20 on the
uplink. The antennas 22 at both base stations 20 were used to
receive and jointly detect the information bits from a first one of
the mobile terminals 30. In this context, it should be noted that
the transmissions from the second mobile terminal 30 creates
spatially correlated interference when detecting the bits from the
first mobile terminal 30. Because of this interference, the
correlation between the i-th and the k-th base stations 20 has the
form R.sub.i,k(f)=g.sub.i.delta.(i-k)+H.sub.i,2(f)H.sub.k,2*(f).
(Eq. 15)
For demodulation processing at a first one of the two base stations
20, the noise correlation matrix R.sub.i,k(f) was estimated using
the following algorithm:
.sub.i,k(f)=.sub.i.delta.(i-k)+.sub.i,2(f).sub.k,2*(f), (Eq. 16)
where .sub.i,j(f)=.sub.i,j.sup.(M,L)(f) when the ML channel
estimator was used, and where .sub.i,j(f)=.sub.i,j.sup.(MAP)(f)
when the proposed channel estimator was used. That is, one
embodiment of the base station 20 used a conventional ML-based
channel estimation process, and another embodiment implemented a
version of the receiver circuit 42, providing channel estimation as
proposed herein. The models and parameters for the full simulation
are given in the table shown in FIG. 6.
With that model/parameter information in mind, the performance
curves shown in FIG. 5 plot the average codeword error rate as a
function of the average signal-to-noise ratio (SNR) at the receiver
of a first one of the two base stations 20. One sees that at an
average codeword error rate of one-percent, the channel estimator
proposed herein outperforms the conventional ML estimator by 2
dB.
Of course, that result and the associated performance simulation
are non-limiting examples of the advantages obtained by use of the
proposed channel estimator. Indeed, the proposed channel estimator
of the present invention is not limited by the foregoing discussion
or by the accompanying illustrations. Rather, it is limited only by
the following appended claims and their legal equivalents.
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